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ARS Home » Pacific West Area » Riverside, California » U.S. Salinity Laboratory » Water Reuse and Remediation Research » Research » Publications at this Location » Publication #180876


item Poss, James
item Russell, Walter
item Grieve, Catherine

Submitted to: Journal of Environmental Quality
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/13/2006
Publication Date: 5/31/2006
Citation: Poss, J.A., Russell, W.B., Grieve, C.M. 2006. Estimating yields of salt- and water-stressed forages with remote sensing in the visible and near infrared. Journal of Environmental Quality. 35:1060-1071.

Interpretive Summary: Alfalfa and wheatgrass are two important forages that can be utilized in areas where soil salinity is a limitation to crop productivity. The ability to monitor or evaluate the efficiency of cropping production systems in saline areas can be dramatically improved by applying remote sensing principles to these important crops. Remotely-sensed ground-based hyperspectral reflectance of salinized and drought stressed alfalfa and wheatgrass canopies at individual wavelengths ranging from the violet visible portion of the electromagnetic spectrum (350nm) to the infrared (1000nm) were used to calculate vegetative indices that were significantly correlated with these stress factors in these crops. Two vegetative indices from a list of 71 evaluated were selected and used to develop a multiple linear regression model predicting fresh weight biomass production of each forage under varying levels of salinity and water stress. The model was calibrated for each forage independently and significantly explained the variation in yields for both forages when subjected to salinity and water stress at different times. The spectral regions of interest were in the visible red and green portion of the spectrum and in the near-infrared.

Technical Abstract: Water shortages are increasing the value of recycled water resources. In arid irrigated regions, the proportion of crop production under deficit irrigation with poorer quality water will increase as demand for fresh water soars and efforts to prevent saline water table development occur. The applicability of remote sensing technology to quantify salinity and water stress effects on forage yield can be an important tool to address yield loss potential when deficit irrigating with poor water quality . In this study, two important forages, alfalfa (Medicago sativa, L.) and tall wheatgrass (Agropyron elongatum, L.), were grown in a volumetric lysimeter facility where rootzone salinity and water content was varied and monitored. Ground-based hyperspectral canopy reflectance in the visible and near infrared (NIR) was related to forage yields from half of the potential combinations of a broad range of salinity and water stress conditions: six salinity treatments (ECiw= 3, 8, 13, 18, 23, and 28 dSm-1) and four different irrigation regimes, based on ratios (0.5, 0.75, 1.0, and 1.25) of local baseline ET for the high irrigation volume, low salinity control treatment. Canopy reflectance spectra were obtained in the 350-1000 nm region from two viewing angles (nadir view, 45 degrees from nadir). Nadir view vegetation indices (VI) were not as strongly correlated with leaf area index changes attributed to water and salinity stress treatments for both alfalfa and wheatgrass. From a list of 71 VIs, two vegetative indices were selected for a multiple linear-regression model that estimated yield under varying salinity and water stress conditions. With data obtained during the middle harvest of a 100-day, three harvest period, regression coefficients for each crop were developed and then used with the model to estimate fresh weights for preceding and succeeding harvests during the same 100 day interval. Alfalfa nitrogen content was influenced by stress whereas wheatgrass nitrogen content was not and the spectral model accounted for this discrepancy to some degree. The model accounted for 72% of the variation in yields in wheatgrass and 94% in yields of alfalfa within the same salinity and water stress treatments. When used to predict the preceding and succeeding harvest yields with the same species-specific coefficients, the model successfully predicted yield in three out of four cases. Correlations between indices and yield increased as canopy development progressed. Remote sensing of growth reductions attributed to simultaneous salinity and water stress were well characterized with VIs obtained from hyperspectral canopy reflectance in the visible and near infrared, but the corrections for effects of varying tissue nitrogen and very low LAI are necessary.